US20100017209A1 - Random voiceprint certification system, random voiceprint cipher lock and creating method therefor - Google Patents

Random voiceprint certification system, random voiceprint cipher lock and creating method therefor Download PDF

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US20100017209A1
US20100017209A1 US12/519,982 US51998207A US2010017209A1 US 20100017209 A1 US20100017209 A1 US 20100017209A1 US 51998207 A US51998207 A US 51998207A US 2010017209 A1 US2010017209 A1 US 2010017209A1
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voiceprint
random
voice
voice data
training
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Kun-Lang Yu
Yen-Chieh Ouyang
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Top Digital Co Ltd
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Top Digital Co Ltd
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04K—SECRET COMMUNICATION; JAMMING OF COMMUNICATION
    • H04K1/00—Secret communication
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
    • G06F21/30—Authentication, i.e. establishing the identity or authorisation of security principals
    • G06F21/31—User authentication
    • G06F21/32—User authentication using biometric data, e.g. fingerprints, iris scans or voiceprints
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
    • G10L17/00—Speaker identification or verification techniques
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
    • G10L17/00—Speaker identification or verification techniques
    • G10L17/02—Preprocessing operations, e.g. segment selection; Pattern representation or modelling, e.g. based on linear discriminant analysis [LDA] or principal components; Feature selection or extraction
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
    • G10L17/00—Speaker identification or verification techniques
    • G10L17/06—Decision making techniques; Pattern matching strategies
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L9/00—Cryptographic mechanisms or cryptographic arrangements for secret or secure communications; Network security protocols
    • H04L9/06—Cryptographic mechanisms or cryptographic arrangements for secret or secure communications; Network security protocols the encryption apparatus using shift registers or memories for block-wise or stream coding, e.g. DES systems or RC4; Hash functions; Pseudorandom sequence generators
    • H04L9/065—Encryption by serially and continuously modifying data stream elements, e.g. stream cipher systems, RC4, SEAL or A5/3
    • H04L9/0656—Pseudorandom key sequence combined element-for-element with data sequence, e.g. one-time-pad [OTP] or Vernam's cipher
    • H04L9/0662—Pseudorandom key sequence combined element-for-element with data sequence, e.g. one-time-pad [OTP] or Vernam's cipher with particular pseudorandom sequence generator
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L9/00—Cryptographic mechanisms or cryptographic arrangements for secret or secure communications; Network security protocols
    • H04L9/32—Cryptographic mechanisms or cryptographic arrangements for secret or secure communications; Network security protocols including means for verifying the identity or authority of a user of the system or for message authentication, e.g. authorization, entity authentication, data integrity or data verification, non-repudiation, key authentication or verification of credentials
    • H04L9/3226—Cryptographic mechanisms or cryptographic arrangements for secret or secure communications; Network security protocols including means for verifying the identity or authority of a user of the system or for message authentication, e.g. authorization, entity authentication, data integrity or data verification, non-repudiation, key authentication or verification of credentials using a predetermined code, e.g. password, passphrase or PIN
    • H04L9/3231—Biological data, e.g. fingerprint, voice or retina

Definitions

  • This method is used in mobile phones or computer related products and can extract the unique feature of the voice by voice spectrum analysis for identifying the user.
  • the primary value of each frame is compared with the boundary set by the user to decide the starting point and end point of the voice.
  • a Princen-Bradley filter is then used to convert the detected voice signals to retrieve corresponding voice spectrum patterns which are compared with reference voice spectrum samples stored previously, thereby identifying the voiceprint of the user.
  • the identification method disclosed in TWN490655 must calculate degrees of matches and distances of gaps for the patterns of sound spectrums.
  • a user can pass the random voiceprint certification system if the calculated distance of gaps does not exceed in the boundaries.
  • there is only one reference sample used by this system so that it is easy for illegal invasion such as proceeding by playing an illegal pre-record voiceprint data.
  • TWN490655 needs further improvement to solve a problem caused by the said single reference sample so the random voiceprint certification system can avoid illegal invasion and enhance security of the random voiceprint certification system.
  • the primary objective of the present invention is to provide a random voiceprint certification system, random voiceprint cipher lock and creating method therefor.
  • a random voiceprint certification system By the random combination of several voiceprint characteristic units, at least one reference voiceprint password is formed.
  • the random voiceprint certification system operation can be carried out and the reliability of the random voiceprint certification system can be improved.
  • the random voiceprint certification system comprises a training system, a random cipher generator, and a testing system.
  • the input raw voice data can be dealt in the training or testing operation.
  • the training system obtains an appointment voiceprint feature model parameter groups from input raw voice data.
  • a plurality of voiceprint characteristic units can be obtained from the appointment voiceprint feature model parameter groups.
  • at least one reference voiceprint password can be obtained to provide the testing system processing the voice testing operation.
  • the random cipher generator generates randomly at least one reference voiceprint password from the appointment voiceprint feature model parameter groups of the voiceprint characteristic units to build the random voiceprint cipher lock.
  • the testing voice data is relative to the reference voiceprint passwords for the requirement of the testing system so that the voice testing operation can be completed.
  • the random voiceprint certification system further comprises a front-end processing portion, and a feature-retrieving portion.
  • the training system retrieved the effective voice data by the front-end processing portion on the input raw voice data.
  • the feature-retrieving portion retrieves features from the effective voice data. The calculation is carried out on the effective voice data to get the most similar path as the appointment voiceprint feature model parameter groups.
  • the testing voice operation the testing system retrieved the effective voice data by the front-end processing portion on the input raw voice data.
  • the feature-retrieving portion retrieves features from the effective voice data. The calculation is carried out for the similar probability between the testing voice feature and the model parameter to output a verification result.
  • the random voiceprint cipher lock comprises a plurality of the voiceprint characteristic units.
  • voiceprint characteristic units By the random combination of voiceprint characteristic units, one or several reference voiceprint passwords are built.
  • reference voiceprint passwords By one or several reference voiceprint passwords, the random voiceprint cipher lock is set up.
  • the testing voice data required by the random voiceprint cipher lock is relative to the reference voiceprint passwords so that the voice testing operation can be completed.
  • the procedures of the random voiceprint cipher lock and creating method therefor of the present invention includes input an input raw voice data; the appointment voiceprint feature model parameter groups can be obtained from the input raw voice data. From the appointment voiceprint feature model parameter groups, the several voiceprint characteristic units are obtained. By one or the several voiceprint characteristic units to built at least one reference voiceprint password, the random voiceprint cipher lock is provided.
  • FIG. 1 is a flowchart of the random voiceprint cipher lock and creating method therefor in accordance with the present invention
  • FIG. 2 is a processing block diagram of the random voiceprint certification system in accordance with the present invention.
  • FIG. 3A is a processing block diagram of the random voiceprint certification system carrying out the voice training operation in accordance with the present invention
  • FIG. 3B is a processing block diagram of the random voiceprint certification system carrying out the voice training operation in accordance with the present invention.
  • FIG. 4A is a relationship diagram illustrating the energy and the frames of the random voiceprint certification system employing short-time energy and zero-crossing rate carrying out endpoint detection in accordance with the present invention
  • FIG. 4B is a partial magnification diagram in accordance with the present invention.
  • FIG. 5A is a relationship diagram illustrating the energy and the frames of the random voiceprint certification system employing the entropy-based algorithm carrying out endpoint testing in accordance with the present invention
  • FIG. 5B is a partial magnification diagram in accordance with the present invention.
  • FIG. 6A is a schematic diagram illustrating the energy timing order of inputting ten voices of the random voiceprint certification system in accordance with the present invention
  • FIG. 6B is a relationship diagram illustrating the energy and the frames of the random voiceprint certification system employing the entropy-based algorithm carrying out endpoint detection according to FIG. 6A shown input voice in accordance with the present invention
  • FIG. 6C is a frames matrix diagram of the random voiceprint certification system employing FIG. 6B shown by endpoint detection to decide selection of the frames in accordance with the present invention
  • FIG. 7 is a schematic diagram illustrating the energy timing order of the random voiceprint certification system by after the completion of cutting point operation, the recombination is carried out or not in accordance with the present invention
  • FIG. 8 is a relationship diagram illustrating the status and the frames of the random voiceprint certification system in accordance with the present invention.
  • FIG. 9 is a schematic diagram illustrating initial distribution models of the status and the frames of the random voiceprint certification system in accordance with the present invention.
  • FIG. 10 is a schematic diagram illustrating status conversion of the random voiceprint certification system in accordance with the present invention.
  • FIG. 11 is a schematic diagram illustrating a most similar path of the random voiceprint certification system in accordance with the present invention.
  • FIG. 12 is a schematic diagram illustrating the equal distinction of the frames of the random voiceprint certification system in accordance with the present invention.
  • FIG. 13 is a schematic diagram illustrating a first redistribution of the frames of the random voiceprint certification system in accordance with the present invention
  • FIG. 14 is a schematic diagram illustrating a second redistribution of the frames of the random voiceprint certification system in accordance with the present invention.
  • FIG. 15 is a schematic diagram illustrating an optimal distribution of the frames of the random voiceprint certification system in accordance with the present invention.
  • FIG. 16 is a schematic diagram illustrating the random cipher generator generating randomly the reference voiceprint passwords of the random voiceprint certification system in accordance with the present invention
  • a random voiceprint cipher lock and creating method thereof in accordance with the present invention comprises three main procedures to build the random voiceprint cipher lock. Furthermore, in general application, the random voiceprint cipher lock and creating method thereof can be applied on the personal electronic data, bank business transactions, and security system of personal recognition.
  • the said three main procedures for the random voiceprint cipher lock and of the creating method includes: a procedure of obtaining input raw voice data S 1 , a procedure of generating appointment voiceprint feature model parameter groups S 2 , and a procedure of generating the random voiceprint cipher lock S 3 .
  • the random voiceprint certification system in accordance with the present invention comprises a training system 10 , a random cipher generator 20 , and a testing system 30 . Therefore, a training or testing operation can be carried out for the input raw voice data.
  • the random voiceprint certification system in accordance with the present invention further comprises: an A/D converter, a voice detector, a front-end processing portion, and a feature-retrieving portion, which are employed to carry out the voice training operation and the voice testing operation.
  • the most important procedure of the voice training operation and the voice testing operation of the present invention is to search position of the voice signal from the input raw voice data, that is, to identify the starting point and terminal point of the voice signal.
  • the better searching method for correct position of the starting point and the terminal point is “endpoint detection”, which includes algorithm based on short-time energy and zero-crossing rate, and entropy-based algorithm.
  • the endpoint detection is firstly carried out in accordance with the energy of the input raw voice data. Additionally, in accordance with the energy of the input raw voice data still, the endpoint detection can be further processed by the entropy-based algorithm. Furthermore, by a cutting operation, the silence and the noise of the input raw voice data can be removed. Finally, After completing the cutting operation, the cut input raw voice data further go through compression and arrangement to obtain the effective voice data.
  • a result of the endpoint detection completed by the algorithm based on both short-time energy and zero-crossing rate shown by a relationship chart of energy vs. frame is illustrated. From the relationship chart of energy vs. frame, several endpoints can be determined. Finally, the random voiceprint certification system can determine each starting point and each terminal point of the input raw voice data.
  • the random voiceprint certification system can also determine each starting point and each terminal point of the input raw voice data through the entropy-based algorithm.
  • the random voiceprint certification system of the present invention can avoid false rejection and false acceptance and thus provide the random voiceprint certification system with good performance in recognition.
  • the random voiceprint certification system of the present invention can prevent the effective voice data from being determined as silence or noise, so as to avoid false rejection; on the other hand, it can also prevent silence and noise from being determined as the effective voice data, so as to avoid false acceptance.
  • an example shows how to process the operation of the endpoint detection by sequentially pronouncing numbers 0 to 9 as the input raw voice data and inputting the input raw voice data into the random voiceprint certification system of the present invention.
  • ten voice signals can be obtained, with each voice signal presenting a sound of one of the pronounced numbers and having a starting point and a terminal point.
  • ten frames which respectively represent the voice signals, can be decided.
  • the operation of the cutting points and a recombination can be processed after determining these frames.
  • a feature value can be obtained from each frame.
  • the operation of the recombination of the frames can be carried out.
  • the random voiceprint certification system can carry out the voiceprint recognition.
  • the random voiceprint certification system 1 checks up the database whether the input account number has been registered. If the account number has not been registered, the procedure is automatically moved to the training system 10 for training and registering voice data for a new account number. But, if the account number has been registered, the procedure is automatically moved to the testing system 30 for verifying whether the features of the input voice match those stored in the account number. At this time, the random cipher generator 20 can randomly generate one or several reference voiceprint passwords, and thus the random voiceprint certification system 1 is completely set up to form the random voiceprint cipher lock for the testing system 30 to process the voice testing operation.
  • a creating method therefor is provided, which preliminary processes the front part of the voice training operation to carry out a step of obtaining the input raw voice data, designated as “S 1 ”. Therefore, users can directly input voice to a voice detector and then can complete the step S 1 for obtaining the input raw voice data. Referring to FIG. 3A , after complete the step S 1 for obtaining the input raw voice data, the input raw voice data can be employed for the next step.
  • the random voiceprint cipher lock and creating method therefor carry out the a step of generating the appointment voiceprint feature model parameter groups, designated as “S 2 ”.
  • the appointment voiceprint feature model parameter groups will be stored in the random voiceprint certification system 1 .
  • the appointment voiceprint feature model parameter groups can be further employed for the next step.
  • the input raw voice data provided by a user for the random voiceprint certification system 1 need to be corresponding to a specific voice sequence, such as the pronunciations of “0, 1, 2, 3, 4, 5, 6, 7, 8, 9, A, B, C, D, E, F, G, H, I, J, K . . . ” etc. Therefore, the training system 10 can generate the appointment voiceprint feature model parameter groups including a plurality of the voiceprint characteristic units.
  • the voiceprint characteristic units relate with 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, A, B, C, D, E, F, G, H, I, J, K . . . etc. one by one.
  • the random cipher generator 20 of the random voiceprint certification system 1 randomly generates at least one reference voiceprint password.
  • the voice testing operation can be carried out according to the at least one reference voiceprint password, which is selected by the random cipher generator 20 .
  • the front-end processing portion retrieves the effective voice data from the input raw voice data and filters non-effective voice data.
  • the short-time energy and zero-crossing rate are employed in the present invention for the endpoint detection.
  • a calculating method combining Gauss possibility distribution is employed, and the equation is as follows:
  • x is the original signal that is divided into a plurality of frames in D-dimension
  • ⁇ i is the expectation value of the background noise signal
  • Equation (2) is simplified and rewritten into equation (3) after obtaining its logarithm.
  • the first 256 points of the front portion of the raw voice data are extracted to calculate the expectation value, variance of the short-time energy and zero-crossing rate.
  • the two values and the raw voice data are substituted into equation (3) for calculation purposes. Since the distributive possibility area of the short-time energy and zero-crossing rate includes the effective voice data and the non-effective voice data, the non-effective voice data can be removed to reduce the amount of data while allowing correct retrieval of the effective voice data.
  • the feature-retrieving portion retrieves voice features from the input voice data
  • the parameters include linear prediction cepstrum coefficient (LPCC) and Mel frequency cepstrum coefficient (MFCC).
  • LPCC linear prediction cepstrum coefficient
  • MFCC Mel frequency cepstrum coefficient
  • Each of the parameters includes twelve cepstral coefficients and twelve delta-cepstral coefficients. Equation (4) is obtained after carrying out partial differentiation on the cepstral coefficients with respect to time:
  • K is the number of considered frames.
  • Cn is the feature value in n-th order
  • L is the total number of the frames in the signal
  • i is the serial number of the frames.
  • FIG. 4 is a schematic diagram illustrating relationship between statuses and frames of the random voiceprint certification system in accordance with the present invention.
  • the term “status” means the change in the mouth shape and the vocal band. Generally, a speaker's mouth has changes in shape while speaking. Thus, each status is the feature of the change of the voice. In some cases, a single sound contains several statuses. The size of the respective status is not fixed like the frame. A status usually includes several or tens of frames.
  • the first status includes three frames
  • the second status includes six frames
  • the third status includes four frames.
  • FIG. 9 is a schematic diagram illustrating initial distribution models of the statuses and the frames of the random voiceprint certification system in accordance with the present invention. For example, three sample voices are equally divided in an initial distribution model.
  • the voices are equally divided for forming frames
  • the residual frame if any, is equally divided into two groups and the result is added into each of the first status and the last status.
  • three factors must be considered in the distribution model: (1) the first frame must belong to the first status, (2) the last frame must belong to the last status, and (3) the status in the frame either remains unchanged or the change of the status in the frame continues to the next one.
  • Gauss distribution possibility is employed to calculate the possibility of each frame of each state, and Viterbi algorithm is employed to obtain the most similar path.
  • FIG. 10 is a schematic diagram illustrating status conversion of the random voiceprint certification system in accordance with the present invention.
  • FIG. 10 shows the possible conversion of the statuses of frames (the number of which is L) when three statuses are involved.
  • the crossed frame is deemed as an impossible status, and the directions indicated by the arrows are the possible paths of the change of the statuses.
  • FIG. 11 is a schematic diagram illustrating a most similar path of the random voiceprint certification system in accordance with the present invention.
  • the most similar path includes a first status having the first, the second, and the third frames, a second status having the fourth, the fifth, and the sixth frames, and a third status having the seventh, the eight, the ninth, and the tenth frames.
  • FIG. 12 is a schematic diagram illustrating division of the frames of the random voiceprint certification system in accordance with the present invention.
  • FIG. 12 shows initial models of three statuses of three sample voices, which are distributions after equal division.
  • the first sample voice is divided equally into three statuses each having three frames, and the residual two frames are divided equally and added into the first status and the second status respectively.
  • the second sample voice is divided equally into three statuses each having four frames.
  • the third sample voice is divided into three statuses each having three frames, and one residual frame is added into the first status. After calculation, the possibility of most similarity is 2156.
  • FIG. 13 is a schematic diagram illustrating a first redistribution of the frames of the random voiceprint certification system in accordance with the present invention. As illustrated in FIG. 13 , the possibility of most similarity has an increase to reach 3171 after the first redistribution.
  • FIG. 14 is a schematic diagram illustrating a second redistribution of the frames of the random voiceprint certification system in accordance with the present invention. As illustrated in FIG. 14 , the possibility most similarity has increase to reach 3571 after the second redistribution.
  • FIG. 15 is a schematic diagram illustrating an optimal distribution of the frames of the random voiceprint certification system in accordance with the present invention. As illustrated in FIG. 15 , the possibility of most similarity cannot be raised after the third distribution. Thus, it can be deemed as the most optimal frame distribution. The expectation value and the variance of each status are calculated to obtain the model parameters that can be stored in the database.
  • equations (1)-(9) are used to obtain the effective training voice features. Viterbi algorithm is then employed to obtain the most similar path. Next, the expectation value and variance of each status are calculated to obtain the appointment voiceprint feature model parameter groups, thereby completing the voice training operation.
  • the training for a user will be ended and rejected if the possibility of the most similarity is smaller than a predetermined threshold. Accordingly, a new training of the random voiceprint certification system 1 is required. Conversely, the training for a user will be approved and ended when the possibility of the most similarity is greater than the predetermined threshold.
  • the appointment voiceprint feature model parameter groups are stored in the random voiceprint certification system 1 .
  • the random voiceprint cipher lock and creating method therefor of the present invention process the procedure of generating the random voiceprint cipher lock S 3 .
  • the random cipher generator 20 randomly generates one or several sets of the reference voiceprint lock from the appointment voiceprint feature model parameter groups.
  • the random voiceprint certification system completes setting up and forms the random voiceprint cipher lock.
  • the random voiceprint cipher lock can provide the testing system 30 to carry out the voice testing operation.
  • the random cipher generator 20 can generate four columns of the reference voiceprint passwords.
  • the column A of the reference voiceprint passwords includes 1279, 2385, A1B2, 9F5U . . . etc.;
  • the column B of the reference voiceprint passwords includes 1357 . . . etc.;
  • the column C of the reference voiceprint passwords includes ABCD . . . etc.;
  • the column D of the reference voiceprint passwords includes 1234 . . . etc.
  • the random voiceprint certification system 1 processes the voice testing operation according to the selection of the reference voiceprint passwords by the random cipher generator.
  • the possibility of similarity between the testing voice features and the reference voiceprint passwords are calculated to obtain the verification result.
  • voice verification when the possibility of least similarity is greater than a predetermined threshold, the user can pass the testing and enter the random voiceprint certification system 1 . Conversely, when the possibility of least similarity is smaller than the predetermined threshold, the testing of the user is failed and ended to exit the random voiceprint certification system 1 .
  • the random voiceprint certification system 1 of the present invention has the random cipher generator 20 to randomly generate one or several reference voiceprint passwords. Therefore, the random voiceprint certification system 1 completes setting and forms the random voiceprint cipher lock. The effect of preventing easy illegal invasion can be achieved.

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CN200610161138.5A CN101197131B (zh) 2006-12-07 2006-12-07 随机式声纹密码验证系统、随机式声纹密码锁及其产生方法
PCT/CN2007/071200 WO2008083571A1 (fr) 2006-12-07 2007-12-06 Système de certification de cryptage aléatoire d'empreinte vocale, verrou de cryptage aléatoire d'empreinte vocale, et procédé de création associé à ceux-ci

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EP2120232A1 (de) 2009-11-18
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